Binance Integrates Bitcoin Lightning Deposits and Withdrawals

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Binance, one of the world’s leading cryptocurrency exchanges, recently completed integration the Lightning Network, a Layer 2 payment protocol that operates on top of Bitcoin. This significant move allows Binance users to take advantage of the LN’s key features for Bitcoin deposits and withdrawals, providing an additional choice of “BTC-Lightning” alongside traditional options like Bitcoin, BEP20, and SegWit. This integration solidifies Binance’s position among other heavyweight exchanges such as Kraken, OKX, Bitfinex, and River Financial, all of whom have adopted this Layer 2 scaling solution.

The need for the Lightning Network integration initially came to light in May when Binance temporarily suspended BTC withdrawals due to a surge in pending transactions and escalating BTC network gas fees. In response, the exchange began running multiple nodes on the Lightning Network in June, aiming to reduce the cost of BTC deposits and withdrawals on its platform. The Lightning Network’s capacity recently surpassed a significant milestone of 5,000 BTC earlier this year.

The growth of the Lightning Network is not restricted to Binance or other similar platforms. Valkyrie Investments’ recent report highlights the rapid uptake of LN in emerging markets such as South America and Africa. Additionally, Lightning Labs, the company responsible for LN, has introduced a new toolkit to help AI applications, such as OpenAI’s ChatGPT, interact with the Bitcoin network. This allows the applications to send, receive, and hold the cryptocurrency, adding another layer of versatility to the burgeoning world of digital currency.

Byte Federal, Inc is currently working on a robust update to its own digital wallet which will include Lightning solutions.

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Alexandra "Alex" Russo, the dynamic Editor-in-Chief of Cryptosphere, is a leading authority in the world of cryptocurrency and blockchain. Born in the bustling city of New York, Alex's interest in digital assets was kindled during her doctoral studies in Computer Science at Stanford University.

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